Contents
Introduction
Tiny Machine Learning (TinyML) [1] is, unsurprisingly, a machine learning technique but this technique is often utilized in building machine learning applications, which require high performance but have limited hardware. a tiny neural network on a microcontroller with really low power requirements (sometimes <1mW).
Figure 1: Tiny ML, the next AI revolution [5]
TinyML is often implemented in low-energy systems such as microcontrollers or sensors to perform automated tasks. One trivial example is the Internet of Things (IoT) devices. However, The biggest challenge in implementing TinyML is that it required “full-stack” engineers or data scientists who have profound knowledge in building hardware, design system architecture, developing software, and applications.
TinyML, IoT, and embedded system
In [2], the Internet of things (IoT) reflects the network of physical objects (a.k.a, things) that are embedded with sensors, software, and other technologies to connect and exchange data with other devices and systems over the Internet. Therefore, most IoT devices should be applied to TinyML to enhance their data collection and data processing. In other words, as argued by many machine learning experts, the relationship between TinyML, IoT, and embedded systems will be a long-lasting (TinyML belongs to IoT).
Applications
In agriculture: Profit losses due to animal illnesses can be reduced by using wearable devices. These smart sensors can help to monitor health vitals such as heart rate, blood pressure, temperature, etc. and TinyML will be useful in predicting on the onslaught of disease and epidemics
In industry: TinyML can prevent downtime due to equipment failure by enabling real-time decisions without human interaction in the manufacturing sector. It can signal workers to perform preventative maintenance when necessary, based on equipment conditions.
In retail: TinyML can help to increase profits in indirect ways by providing effective means for warehouse or store monitoring. As smart sensors will possibly become popular in the future, they could be utilized in small stores, supermarkets, or hypermarkets to monitor shelves in-store. TinyML will be useful in processing those data and prevent items from becoming out of stock. Humans will enjoy endless amusement came from these ML-based applications for the economic sector.
In mobility: TinyML will help sensors have more power in ingesting real-time traffic data. Once those sensors are applied in reality, humans will be no longer worry about traffic-related issues (such as traffic jams, traffic accidents)
Imagine when all sensors in the embedded systems mentioned in the above applications are connected in a super-fast Internet connection, every TinyML algorithm will be controlled by a giant ML system. That is a time when humans can take advantage of computer power in performing boring tasks. We, certainly, feel happier, have more chances for our family, and have more time to come up with important decisions.
First glance at the potential of TinyML
According to a survey done by ABI [3], by 2030, there are almost 250 billion microcontrollers in our printers, TVs, cars, and pacemakers that can now perform tasks that previously only our computers and smartphones could handle. All of our devices and appliances are getting smarter thanks to microcontrollers. In addition, in [4] Silent Intelligence also predicts that TinyML can reach more than $ 70 billion in economic value at the end of 2025. From 2016 to 2020, the number of microcontrollers (MCU) was increasing rapidly, and this figure is predicted to rise in the next 3 years.